Question

因此，NetworkX明确表示他们在n ^ 2时间内使用算法生成随机几何图形。他们说使用K-D树可以实现更快的算法。我的问题是如何尝试实现此算法的K-D树版本？我不熟悉这种数据结构，也不称自己为python专家。试图解决这个问题。感谢所有帮助，谢谢！

    def random_geometric_graph(n, radius, dim=2, pos=None):
        G=nx.Graph()
        G.name="Random Geometric Graph"
        G.add_nodes_from(range(n)) 
        if pos is None:
            # random positions
            for n in G:
                G.node[n]['pos']=[random.random() for i in range(0,dim)]
        else:
            nx.set_node_attributes(G,'pos',pos)
        # connect nodes within "radius" of each other
        # n^2 algorithm, could use a k-d tree implementation
        nodes = G.nodes(data=True)
        while nodes:
            u,du = nodes.pop()
            pu = du['pos']
            for v,dv in nodes:
                pv = dv['pos']
                d = sum(((a-b)**2 for a,b in zip(pu,pv)))
                if d <= radius**2:
                    G.add_edge(u,v)
    return G

Answer 1

这是一种使用@tcaswell上面提到的scipy KD-tree实现的方法。

import numpy as np
from scipy import spatial
import networkx as nx
import matplotlib.pyplot as plt
nnodes = 100
r = 0.15
positions =  np.random.rand(nnodes,2)
kdtree = spatial.KDTree(positions)
pairs = kdtree.query_pairs(r)
G = nx.Graph()
G.add_nodes_from(range(nnodes))
G.add_edges_from(list(pairs))
pos = dict(zip(range(nnodes),positions))
nx.draw(G,pos)
plt.show()

使用K-D树实现NetworkX随机几何图形

1 个答案: